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A Simple Model for Assessing Millimeter-Wave Attenuation in Brownout Conditions
Arkadi Zilberman1,2, Natan Kopeika1
1School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel.
Helicopter brownout conditions pose risks during landing. Millimeter-wave (mm-wave) radar shows potential for safe navigation through dust clouds by evaluating signal attenuation and power transfer.
Area of Science:
- Aerospace Engineering
- Electromagnetics
- Atmospheric Physics
Background:
- Helicopter operations in arid regions face hazards from brownout conditions, where rotor downwash creates dense dust clouds.
- Visible and infrared sensors are ineffective in brownout, necessitating alternative navigation technologies.
- Millimeter-wave (mm-wave) radar offers potential for penetrating atmospheric obscurants like dust.
Purpose of the Study:
- To theoretically evaluate mm-wave attenuation, scattering, and power transfer within helicopter brownout conditions.
- To model the impact of sand grain clouds on mm-wave propagation up to 50 meters.
- To validate the theoretical model against field data and existing literature.
Main Methods:
- Development of a theoretical model for mm-wave (85-100 GHz) attenuation and scattering in sand clouds.
- Calculation of radiant flux and power transfer to the receiver.
- Comparison of model predictions with empirical data from brownout environments.
Main Results:
- The study provides predictions for mm-wave signal behavior in simulated brownout scenarios.
- A simple model for estimating mm-wave power transfer demonstrated good agreement with measured values.
- The research quantifies signal attenuation and scattering effects relevant to helicopter navigation.
Conclusions:
- Mm-wave radar systems show promise for enabling safe helicopter operations in challenging brownout conditions.
- Theoretical modeling provides a valuable tool for understanding and predicting mm-wave propagation in dust environments.
- Further research and validation are supported by the satisfactory agreement between the model and real-world measurements.
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